Intelligent agent

In artificial intelligence, an intelligent agentIA is an autonomous entity which observes through sensors and acts upon an environment using actuators ie it is an agent and directs its activity towards achieving goals ie it is "rational", as defined in economics[1] Intelligent agents may also learn or use knowledge to achieve their goals They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent[2]
Simple reflex agent

Intelligent agents are often described schematically as an abstract functional system similar to a computer program For this reason, intelligent agents are sometimes called abstract intelligent agentsAIA[citation needed] to distinguish them from their real world implementations as computer systems, biological systems, or organizations Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents Still others notably Russell & Norvig 2003 considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, "rational agent"

Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations

Intelligent agents are also closely related to software agents an autonomous computer program that carries out tasks on behalf of users In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent by Russell and Norvig's definition For example, autonomous programs used for operator assistance or data mining sometimes referred to as bots are also called "intelligent agents"

Contents

1 A variety of definitions

2 Structure of agents

3 Architectures for Intelligent Agents

4 Classes of intelligent agents

41 Simple reflex agents

42 Model-based reflex agents

43 Goal-based agents

44 Utility-based agents

45 Learning agents

46 Other classes of intelligent agents

5 Hierarchies of agents

6 Applications

7 See also

8 Notes

9 References

10 External links

A variety of definitions

Intelligent agents have been defined many different ways[3] According to Nikola Kasabov[4] AI systems should exhibit the following characteristics:

Accommodate new problem solving rules incrementally

Adapt online and in real time

Are able to analyze itself in terms of behavior, error and success

Learn and improve through interaction with the environment embodiment

Learn quickly from large amounts of data

Have memory-based exemplar storage and retrieval capacities

Have parameters to represent short and long term memory, age, forgetting, etc

Structure of agents

A simple agent program can be defined mathematically as an agent function[5] which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions:

f
:
P
∗
→
A
\rightarrow A

Agent function is an abstract concept as it could incorporate various principles of decision making like calculation of utility of individual options, deduction over logic rules, fuzzy logic, etc[6]

The program agent, instead, maps every possible percept to an action

We use the term percept to refer to the agent's perceptional inputs at any given instant In the following figures an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

Architectures for Intelligent Agents

Weiss 2013 said we should consider four classes of agents:

Logic-based agents - in which the decision about what action to perform is made via logical deduction;

Reactive agents - in which decision making is implemented in some form of direct mapping from situation to action;

Belief-desire-intention agents - in which decision making depends upon the manipulation of data structures representing the beliefs, desires, and intentions of the agent; and finally,

Layered architectures - in which decision making is realized via various software layers, each of which is more or less explicitly reasoning about the environment at different levels of abstraction

Classes of intelligent agents

Russell & Norvig 2003 group agents into five classes based on their degree of perceived intelligence and capability:[7]

simple reflex agents

model-based reflex agents

goal-based agents

utility-based agents

learning agents

Simple reflex agents

Simple reflex agents act only on the basis of the current percept, ignoring the rest of the percept history The agent function is based on the condition-action rule: if condition then action

This agent function only succeeds when the environment is fully observable Some reflex agents can also contain information on their current state which allows them to disregard conditions whose actuators are already triggered

Infinite loops are often unavoidable for simple reflex agents operating in partially observable environments Note: If the agent can randomize its actions, it may be possible to escape from infinite loops

Model-based reflex agents

A model-based agent can handle partially observable environment Its current state is stored inside the agent maintaining some kind of structure which describes the part of the world which cannot be seen This knowledge about "how the world works" is called a model of the world, hence the name "model-based agent"

A model-based reflex agent should maintain some sort of internal model that depends on the percept history and thereby reflects at least some of the unobserved aspects of the current state Percept history and impact of action on the environment can be determined by using internal model It then chooses an action in the same way as reflex agent

Goal-based agents

Goal-based agents further expand on the capabilities of the model-based agents, by using "goal" information Goal information describes situations that are desirable This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state Search and planning are the subfields of artificial intelligence devoted to finding action sequences that achieve the agent's goals

it is more flexible because the knowledge that supports its decisions is represented explicitly and can be modified

Utility-based agents

Goal-based agents only distinguish between goal states and non-goal states It is possible to define a measure of how desirable a particular state is This measure can be obtained through the use of a utility function which maps a state to a measure of the utility of the state A more general performance measure should allow a comparison of different world states according to exactly how happy they would make the agent The term utility can be used to describe how "happy" the agent is

A rational utility-based agent chooses the action that maximizes the expected utility of the action outcomes - that is, what the agent expects to derive, on average, given the probabilities and utilities of each outcome A utility-based agent has to model and keep track of its environment, tasks that have involved a great deal of research on perception, representation, reasoning, and learning

Learning agents

Learning has the advantage that it allows the agents to initially operate in unknown environments and to become more competent than its initial knowledge alone might allow The most important distinction is between the "learning element", which is responsible for making improvements, and the "performance element", which is responsible for selecting external actions

The learning element uses feedback from the "critic" on how the agent is doing and determines how the performance element should be modified to do better in the future The performance element is what we have previously considered to be the entire agent: it takes in percepts and decides on actions

The last component of the learning agent is the "problem generator" It is responsible for suggesting actions that will lead to new and informative experiences

Other classes of intelligent agents

According to other sources[who], some of the sub-agents not already mentioned in this treatment may be a part of an Intelligent Agent or a complete Intelligent Agent They are:

Decision Agents that are geared to decision making;

Input Agents that process and make sense of sensor inputs – eg neural network based agents;

Processing Agents that solve a problem like speech recognition;

Spatial Agents that relate to the physical real-world;

World Agents that incorporate a combination of all the other classes of agents to allow autonomous behaviors

Believable agents - An agent exhibiting a personality via the use of an artificial character the agent is embedded for the interaction

Physical Agents - A physical agent is an entity which percepts through sensors and acts through actuators

Temporal Agents - A temporal agent may use time based stored information to offer instructions or data acts to a computer program or human being and takes program inputs percepts to adjust its next behaviors

Hierarchies of agents

Main article: Multi-agent system

To actively perform their functions, Intelligent Agents today are normally gathered in a hierarchical structure containing many “sub-agents” Intelligent sub-agents process and perform lower level functions Taken together, the intelligent agent and sub-agents create a complete system that can accomplish difficult tasks or goals with behaviors and responses that display a form of intelligence

Applications

An example of an automated online assistant providing automated customer service on a webpage

Intelligent agents are applied as automated online assistants, where they function to perceive the needs of customers in order to perform individualized customer service Such an agent may basically consist of a dialog system, an avatar, as well an expert system to provide specific expertise to the user[8]

See also

Agent disambiguation

Cognitive architectures

Cognitive radio – a practical field for implementation

Cybernetics, Computer science

Data mining agent

Embodied agent

Federated search – the ability for agents to search heterogeneous data sources using a single vocabulary

Fuzzy agents – IA implemented with adaptive fuzzy logic

GOAL agent programming language

Intelligence

Intelligent system

JACK Intelligent Agents

Multi-agent system and multiple-agent system – multiple interactive agents

PEAS classification of an agent's environment

Reinforcement learning

Semantic Web – making data on the Web available for automated processing by agents

Simulated reality

Social simulation

Notes

^ Russell & Norvig 2003, chpt 2

^ According to the definition given by Russell & Norvig 2003, chpt 2

^ Some definitions are examined by Franklin & Graesser 1996 and Kasabov 1998

References

Stan Franklin and Art Graesser 1996; Is it an Agent, or just a Program: A Taxonomy for Autonomous Agents; Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Springer-Verlag, 1996